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https://github.com/deepfakes/faceswap.git
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torzdf
112 lines
3.6 KiB
Python
112 lines
3.6 KiB
Python
#!/usr/bin/env python3
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"""CV2 DNN landmarks extractor for faceswap.py
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Adapted from: https://github.com/yinguobing/cnn-facial-landmark
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MIT License
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Copyright (c) 2017 Yin Guobing
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.
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"""
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from __future__ import annotations
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import logging
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import cv2
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import numpy as np
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from lib.utils import get_module_objects, GetModel
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from plugins.extract.base import ExtractPlugin
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logger = logging.getLogger(__name__)
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class CV2DNNAlign(ExtractPlugin):
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"""CV2 DNN Plugin for face alignment """
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def __init__(self) -> None:
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# pylint:disable=duplicate-code
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super().__init__(input_size=128,
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batch_size=1,
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is_rgb=True,
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dtype="float32",
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scale=(0, 255))
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self.model: cv2.dnn.Net
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def load_model(self) -> cv2.dnn.Net:
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"""Load the CV2 DNN Aligner Model
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Returns
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-------
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The loaded cv2-DNN model
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"""
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weights = GetModel(model_filename="cnn-facial-landmark_v1.pb", git_model_id=1)
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model_path = weights.model_path
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assert isinstance(model_path, str)
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model = cv2.dnn.readNetFromTensorflow(model_path)
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model.setPreferableTarget(cv2.dnn.DNN_TARGET_CPU)
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return model
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def pre_process(self, batch: np.ndarray) -> np.ndarray:
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"""Format the ROI faces detection boxes for prediction
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Parameters
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----------
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batch
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The batch of face detection bounding boxes as (bs, l, t, r, b)
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Returns
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-------
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The face detection bounding boxes formatted to take an image patch for prediction
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"""
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heights = batch[..., 3] - batch[..., 1]
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widths = batch[..., 2] - batch[..., 0]
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diff_height_width = widths - heights
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offset = np.abs(diff_height_width // 2)
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batch[:, [1, 3]] += offset[:, None]
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cx = (batch[:, 0] + batch[:, 2]) // 2
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cy = (batch[:, 1] + batch[:, 3]) // 2
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size = np.maximum(widths, heights)
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half = size // 2
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retval = batch.copy()
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retval[:, 0] = cx - half
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retval[:, 1] = cy - half
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retval[:, 2] = retval[:, 0] + size
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retval[:, 3] = retval[:, 1] + size
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return retval
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def process(self, batch: np.ndarray) -> np.ndarray:
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"""Predict the 68 point landmarks
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Parameters
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----------
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feed
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The batch to feed into the aligner
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Returns
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-------
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The predictions from the aligner
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"""
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self.model.setInput(batch.transpose((0, 3, 1, 2)))
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return self.model.forward().reshape(batch.shape[0], -1, 2)
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__all__ = get_module_objects(__name__)
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